autotab
1.0.1Variational Autoencoders for Heterogeneous Tabular Data
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Overview
About
Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' interface, enabling reproducible VAE training for heterogeneous tabular datasets.
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CRAN checks
13OK
Slowest check: 1.1 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
5
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Check history
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
Documentation
READMEYes · 603 wordsVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
- Examples that run
- 80%
- Documented parameters
- 97%
- Return-value docs
- 92%
- References docs
- 0%
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3.2K
CRAN downloads in the past year
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30930 days
1.4K90 days
3.2K1 year
Also on99 r2u50 autocran
Repository
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Last activity 2026-05-21
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Checks run against github.com/sarahmilligan-hub/autotab on 2026-07-26.
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README.Rmd
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Dependencies
Declared dependencies
6 external dependencies (excludes base and recommended)
Depends (1)
R >= 4.1
Imports (5)
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none
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Package metadata
- First published
- 2025-11-24
- Total releases
- 5 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1
- Bundled data
- 388 KB / 1 file
- Download size
- 33 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet